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Forecasting International Tourism Demand in China Mainland Based on Comparison of Three Models

Mei Li


Tourism is an important part of the national economy, accuracy forecasting tourism demand is conducive to promoting the sustainable development of the tourism industry. In order to forecast international tourism demand in Mainland China, this paper uses the monthly tourist arrivals from Mainland China to Thailand, Japan and Korea time span from January 2011 to December 2019, and consider Baidu search engine as exogenous variable. Using three commonly used forecasting models, namely, seasonal autoregressive integrated moving average with exogenous variable (SARMAX) model, back propagation neural network (BPNN) model and support vector regression (SVR) model to long term and short term forecast the international tourism demand in Mainland China. The results show that the SARIMAX model generate highest prediction accuracy for almost all evaluation indicators and forecasting steps, while BPNN model and SVR model show different forecasting accuracy under different conditions, which provides guidance for the selection of forecasting models for tourism demand.


SARIMAX Model; BPNN Model; SVR Model; Tourism Demand Forecasting

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Abellana, D. P. M., Rivero, D. M. C., Aparente, M. E., & Rivero, A. (2021). Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: A Philippine scenario. Journal of Tourism Futures. 7(1), 78-97.

Akin, M. (2015). A novel approach to model selection in tourism demand modeling. Tourism Management, 48, 64-72.

Ampountolas, A. (2021). Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models. Forecasting, 3(3), 580-595.

Bangwayo-Skeete, P.F., & Skeete, R.W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management, 46, 454-464.

Bokelmann, B., & Lessmann, S. (2019). Spurious patterns in Google Trends data-An analysis of the effects on tourism demand forecasting in Germany. Tourism management, 75, 1-12.

Cang, S. (2014). A comparative analysis of three types of tourism demand forecasting models: Individual, linear combination and non‐linear combination. International Journal of Tourism Research, 16(6), 596-607.

Cho, V. (2003). A comparison of three different approaches to tourist arrival forecasting. Tourism management, 24(3), 323-330.

Claveria, O., Monte, E., & Torra, S. (2015). Tourism Demand Forecasting with Neural Network Models: Different Ways of Treating Information. International Journal of Tourism Research, 17(5), 492-500.

Dinis, G., Breda, Z., Costa, C., & Pacheco, O. (2019). Google Trends in tourism and hospitality research: a systematic literature review. Journal of Hospitality and Tourism Technology.

Fajardo-Toro, CH., Mula, J., & Poler, R. (2019). Adaptive and hybrid forecasting models—A review. Engineering Digital Transformation, 315–322.

Feng, Y., Li, G., Sun, X., & Li, J. (2019). Forecasting the number of inbound tourists with Google Trends. Procedia Computer Science, 162, 628-633.

Lee, G. C., & Choi, S. H. (2020). Forecasting Foreign Visitors using SARIMAX Models with the Exogenous Variable of Demand Decrease. Journal of the Society of Korea Industrial and Systems Engineering, 43(4), 59-66.

Li, S., Chen, T., Wang, L., & Ming, C. (2018). Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index. Tourism Management, 68, 116-126.

Li, X., Wu, Q., Peng, G., & Lv, B. (2016). Tourism forecasting by search engine data with noise-processing. African Journal of Business Management, 10(6), 114-130.

Liao, Z., Jin, M., Luo, Y., Ren, P., & Gao, H. (2013). Research on prediction of tourists’ quantity in Jiuzhaigou Valley scenic based on ABR@ G integration model. International Journal of Environment and Pollution, 51(34), 176–191.

Lin, K. P., Pai, P. F., Lu, Y. M., & Chang, P. T. (2013). Revenue forecasting using a least-squares support vector regression model in a fuzzy environment. Information Sciences, 220, 196–209.

Liu, H., Liu, Y., Li, G., & Wen, L. (2021). Tourism demand nowcasting using a LASSO-MIDAS model. International Journal of Contemporary Hospitality Management.

Nontapa, C., Kesamoon, C., Kaewhawong, N., & Intrapaiboon, P. (2020, November). A new time series forecasting using decomposition method with SARIMAX model. In International Conference on Neural Information Processing (pp. 743-751).

Oh, CO., & Morzuch, BJ. (2005). Evaluating Time-Series Models to Forecast the Demand for Tourism in Singapore: Comparing Within-Sample And Postsample Results. Journal of Travel Research, 43(4), 404–413.

Park, E., Park, J., & Hu, M. (2021). Tourism demand forecasting with online news data mining. Annals of Tourism Research, 90, 103273.

Prilistya, S. K., Permanasari, A. E., & Fauziati, S. (2021, August). The Effect of The COVID-19 Pandemic and Google Trends on the Forecasting of International Tourist Arrivals in Indonesia. In 2021 IEEE Region 10 Symposium (TENSYMP) (pp. 1-8). IEEE.

Shen, S., Li, G., & Song, H. (2011). Combination forecasts of international tourism demand. Annals of Tourism Research, 38(1), 72-89.

Song, H., Qiu, R. T., & Park, J. (2019). A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting. Annals of Tourism Research, 75, 338-362.

Song, H., Wong, K. F., & Chon, K. S. (2003). Modelling and forecasting the demand for Hong Kong tourism. International Journal of Hospitality Management, 22, 435–451.

Sun, X., Sun, W., Wang, J., Zhang, Y., & Gao, Y. (2016). Using a Grey–Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China. Tourism Management, 52, 369–379.

Tsui, W. H. K., & Balli, F. (2017). International arrivals forecasting for Australian airports and the impact of tourism marketing expenditure. Tourism Economics, 23(2), 403-428.

Wen, L., Liu, C., Song, H., & Liu, H. (2021). Forecasting tourism demand with an improved mixed data sampling model. Journal of Travel Research, 60(2), 336-353.

Wong, K. K., Song, H., Witt, S. F., & Wu, D. C. (2007). Tourism forecasting: to combine or not to combine?. Tourism management, 28(4), 1068-1078.

Wu, D., Song, H., & Shen, S. (2017). New developments in tourism and hotel demand modeling and forecasting. International Journal of Contemporary Hospitality Management, 29(1), 507-529.

DOI: http://dx.doi.org/10.18686/fm.v8i2.6345